Andrew Ng engagement report
@AndrewYNg - 1.8M followers on X
Measured over 5 original posts from a 30-day window, last computed on August 31, 2026.
Engagement
Early reading. We have captured 5 original posts for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.
A typical post picks up 14K interactions against 1.8M followers, an engagement rate of 0.785%. Posts are seen about 2.0M times each, and 0.713% of those impressions turn into an interaction. That is about 109.9% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.27 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 16:00 UTC, and Friday is the busiest day of the week. Of the 5 posts sampled, 20% carry an image or video and 100% link out. The account's strongest tracked post pulled 44K interactions, about 2.3x its own typical post. Only 5 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
Measured over 5 original posts from a 30-day window, last computed on August 31, 2026.
Where this sits in the catalog
At 0.785%, Andrew Ng sits above the 75th percentile of the 36,261 accounts in this comparison. That places it in the top 25% band, which runs 0.431% to 2.10%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.08% |
| 75th percentile | 0.431% |
| 90th percentile | 2.10% |
| 99th percentile | 160.7% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 16:00 UTC, and Friday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 50K |
| 01:00 UTC | -2% | 51K |
| 02:00 UTC | -3% | 50K |
| 03:00 UTC | -4% | 53K |
| 04:00 UTC | -6% | 43K |
| 05:00 UTC | -4% | 42K |
| 06:00 UTC | -4% | 48K |
| 07:00 UTC | -5% | 51K |
| 08:00 UTC | -4% | 60K |
| 09:00 UTC | -3% | 69K |
| 10:00 UTC | -2% | 71K |
| 11:00 UTC | -3% | 78K |
| 12:00 UTC | -2% | 86K |
| 13:00 UTC | -2% | 93K |
| 14:00 UTC | -3% | 96K |
| 15:00 UTC | -2% | 100K |
| 16:00 UTC | -3% | 97K |
| 17:00 UTC | -2% | 90K |
| 18:00 UTC | -1% | 84K |
| 19:00 UTC | -2% | 79K |
| 20:00 UTC | -1% | 74K |
| 21:00 UTC | -1% | 66K |
| 22:00 UTC | -2% | 57K |
| 23:00 UTC | -2% | 51K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +4% | 229K |
| Monday | 0% | 284K |
| Tuesday | -2% | 273K |
| Wednesday | -1% | 250K |
| Thursday | -2% | 243K |
| Friday | -3% | 251K |
| Saturday | +3% | 226K |
Best tweets
- Jul 27, 20262.3x their median
Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community. During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion. That’s why we created the Open Secure AI Alliance.
- Aug 14, 20261.9x their median
New: A map of the most important skills in AI Engineering. https://t.co/VVkn1Dqp1N
- Aug 5, 20261.8x their median
Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at: https://t.co/Rv3LMdLluK
- May 22, 2026
The new White House policy requiring green card applicants to apply from outside the US is a capricious attack on legal immigration. It will hurt families, leave us with fewer doctors, teachers and scientists, and hurt American competitiveness in AI.
- Aug 21, 2026
The most important skills in Building and Deploying AI Applications. https://t.co/IyWIKLIzeM
- Jul 28, 2026
Fifteen years ago, @Coursera and online courses changed education. It worked better than almost anyone expected, expanding access by opening up where you can learn. But how you learn remains largely the same as it has for centuries: it is still one-size-fits-all, taught the same way to each person who shows up. We now have an opportunity to change how learning happens. With advances in AI, we can now build a custom learning guide for each person. We will turn learning from one‑to‑many to one‑to‑one. I'm starting LearnVector to invent this next generation of learning. We are starting with a $100M investment from Coursera, and plan to collaborate closely with Coursera and Udemy. Good learning needs much more than just a chatbot. Research shows that chatbots without guardrails harm learning. They help complete tasks and enable students to do better on homework. But cognitive offloading to a chatbot results in them being less skilled. And, you cannot always trust what a chatbot tells you. In contrast, LearnVector will plan a path with you, adapt to how you learn, and patiently stay with you until you’ve mastered new skills. One thing has not changed in all this time. People want learning they can trust: material that is accurate, relevant, and worth the effort you put into it. Anything less wastes the most valuable thing a learner has: time. Coursera has a trusted library of materials from authoritative sources. LearnVector plans to work with Coursera to bring this trustworthy learning to everyone. I'm grateful to Greg Hart and the entire Coursera team for supporting LearnVector. I look forward to working with our talented team to change how we learn, and accelerate human development. https://t.co/TqFUDFd1hb
- Jul 23, 2026
Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose. @rohitcprasad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think! Try it out: https://t.co/P0mGnI1o31 (requires your own API key) Source code: https://t.co/NYCiTD6hSq
- Jun 30, 2026
“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system. External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent. With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both! I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering). [Original text: The Batch]
- Aug 28, 2026
How have software engineering fundamentals changed with agentic coding? Here is our AI Engineering Skills map for software engineering fundamentals. https://t.co/cnRLj43DLs
- Aug 6, 2025
I'm thrilled to announce the definitive course on Claude Code, created with @AnthropicAI and taught by Elie Schoppik @eschoppik. If you want to use highly agentic coding - where AI works autonomously for many minutes or longer, not just completing code snippets - this is it. Claude Code has been a game-changer for many developers (including me!), but there's real depth to using it well. This comprehensive course covers everything from fundamentals to advanced patterns. After this short course, you'll be able to: - Orchestrate multiple Claude subagents to work on different parts of your codebase simultaneously - Tag Claude in GitHub issues and have it autonomously create, review, and merge pull requests - Transform messy Jupyter notebooks into clean, production-ready dashboards - Use MCP tools like Playwright so Claude can see what's wrong with your UI and fix it autonomously Whether you're new to Claude Code or already using it, you'll discover powerful capabilities that can fundamentally change how you build software. I'm very excited about what agentic coding lets everyone now do. Please take this course! https://t.co/HGM8ArDalK
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
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Reading these numbers
A typical post picks up 14K interactions against 1.8M followers, an engagement rate of 0.785%. Posts are seen about 2.0M times each, and 0.713% of those impressions turn into an interaction. That is about 109.9% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.27 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 16:00 UTC, and Friday is the busiest day of the week. Of the 5 posts sampled, 20% carry an image or video and 100% link out. The account's strongest tracked post pulled 44K interactions, about 2.3x its own typical post. Only 5 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
- What is Andrew Ng's engagement rate on X?
- Andrew Ng (@AndrewYNg) has an engagement rate of 0.785%, based on the median interactions across 5 original posts from the last 30 days against 1,847,478 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.785%, Andrew Ng sits above the 75th percentile of the 36,261 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @AndrewYNg have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @AndrewYNg post?
- Most posts go out around 16:00 UTC, and Friday is its busiest day, at roughly 0.27 posts per day across the measured window.